Consensus-based ranking of multivalued objects: A generalized borda count approach

Zhang, Y; Zhang, W; Pei, J; Lin, X; Lin, Q; Li, A

Consensus-based ranking of multivalued objects: A generalized borda count approach

Zhang, Y

Zhang, W

Pei, J Lin, X

Lin, Q Li, A

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2014, 26 (1), pp. 83 - 96
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Zhang, W https://orcid.org/0000-0001-6572-2600	en_US
dc.contributor.author	Pei, J	en_US
dc.contributor.author	Lin, X https://orcid.org/0000-0003-2396-7225	en_US
dc.contributor.author	Lin, Q	en_US
dc.contributor.author	Li, A	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2014, 26 (1), pp. 83 - 96	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32985
dc.description.abstract	In this paper, we tackle a novel problem of ranking multivalued objects, where an object has multiple instances in a multidimensional space, and the number of instances per object is not fixed. Given an ad hoc scoring function that assigns a score to a multidimensional instance, we want to rank a set of multivalued objects. Different from the existing models of ranking uncertain and probabilistic data, which model an object as a random variable and the instances of an object are assumed exclusive, we have to capture the coexistence of instances here. To tackle the problem, we advocate the semantics of favoring widely preferred objects instead of majority votes, which is widely used in many elections and competitions. Technically, we borrow the idea from Borda Count (BC), a well-recognized method in consensus-based voting systems. However, Borda Count cannot handle multivalued objects of inconsistent cardinality, and is costly to evaluate top (k) queries on large multidimensional data sets. To address the challenges, we extend and generalize Borda Count to quantile-based Borda Count, and develop efficient computational methods with comprehensive cost analysis. We present case studies on real data sets to demonstrate the effectiveness of the generalized Borda Count ranking, and use synthetic and real data sets to verify the efficiency of our computational method. © 1989-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2012.250	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Consensus-based ranking of multivalued objects: A generalized borda count approach	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	26	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

In this paper, we tackle a novel problem of ranking multivalued objects, where an object has multiple instances in a multidimensional space, and the number of instances per object is not fixed. Given an ad hoc scoring function that assigns a score to a multidimensional instance, we want to rank a set of multivalued objects. Different from the existing models of ranking uncertain and probabilistic data, which model an object as a random variable and the instances of an object are assumed exclusive, we have to capture the coexistence of instances here. To tackle the problem, we advocate the semantics of favoring widely preferred objects instead of majority votes, which is widely used in many elections and competitions. Technically, we borrow the idea from Borda Count (BC), a well-recognized method in consensus-based voting systems. However, Borda Count cannot handle multivalued objects of inconsistent cardinality, and is costly to evaluate top (k) queries on large multidimensional data sets. To address the challenges, we extend and generalize Borda Count to quantile-based Borda Count, and develop efficient computational methods with comprehensive cost analysis. We present case studies on real data sets to demonstrate the effectiveness of the generalized Borda Count ranking, and use synthetic and real data sets to verify the efficiency of our computational method. © 1989-2012 IEEE.

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http://hdl.handle.net/10453/32985